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Adaptive associative classification with emerging frequent patterns

High Technology Letters 2012年第1期18卷 38-44页

作者： Wang Xiaofeng Zhang Dapeng Shi Zhongzhi The Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190P.R.China Graduate University of Chinese Academy of Sciences Beijing 100049P.R.China Institute of Information Science and Engineering Yanshan UniversityQinhuangdao 066004P.R.China

In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ a support vector machine（SVM） based method to refine the discovered emerging ~equent patterns for classification rule extension for class label prediction. The empirical study shows that our method can be used to classify increasing resources efficiently and effectively.

关键词： associative classification rule frequent pattern mining emerging frequent pattern supportvector machine （SVM）

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Theory of Pump-Probe Experiments of Metallic Metamaterials Coupled to a Gain Medium

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Physical Review Letters 2012年第18期108卷 187402-187402页

作者： Zhixiang Huang Th. Koschny C. M. Soukoulis Ames Laboratory and Department of Physics and Astronomy Iowa State University Ames Iowa 50011 USA Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei 230039 China Institute of Electronic Structure and Laser FORTH 71110 Heraklion Crete Greece

We establish a new approach for pump-probe simulations of metallic metamaterials coupled to the gain materials. It is of vital importance to understand the mechanism of the coupling of metamaterials with the gain medium. Using a four-level gain system, we have studied light amplification of arrays of metallic split-ring resonators with a gain layer underneath. We find that the differential transmittance ΔT/T can be negative for split-ring resonators on the top of the gain substrate, which is not expected, and ΔT/T is positive for the gain substrate alone. These simulations agree with pump-probe experiments and can help to design new experiments to compensate for the losses of metamaterials.

关键词： Metamaterials

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Rate-embedded differential space-frequency codes based on cyclic delay diversity

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Journal of Computational Information Systems 2012年第1期8卷 151-158页

作者： Zhang, Hongwei Xu, Lei Li, Xiaohui Lu, Lu Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China Anhui Public Security Department Hefei 230061 China

The conventional differential space-frequency codes (DSFC) based on cyclic delay diversity (CDD) only could achieve the same transmission rate with that of a single transmit antenna system, the spectral efficiency was low. To improve the spectral efficiency in a non-coherent multiple antenna system, the advantage of CDD is considered fully in this paper and then a rate-embedded differential space-frequency coding scheme based on grouped CDD is proposed. In the proposed scheme, all transmit antennas are partitioned into several groups firstly, the conventional cyclic delays are adopted to achieve spatial diversity in each group, those symbols among different groups are transmitted differentially with low rate and high rate message matrices alternately, consequently high rate transmission is obtained. Simulation results verify that, compared with the conventional DSFC based on CDD, the proposed coding scheme can achieve obviously higher transmission rates and better error performances. © 2012 Binary Information Press January 2012.

关键词： Spectrum efficiency

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Multi-task Semi-supervised Semantic Feature Learning for Classification

Multi-task Semi-supervised Semantic Feature Learning for Cla...

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IEEE International Conference on Data Mining (ICDM)

作者： Changying Du Fuzhen Zhuang Qing He Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Multi-task learning has proven to be useful to boost the learning of multiple related but different tasks. Meanwhile, latent semantic models such as LSA and LDA are popular and effective methods to extract discriminative semantic features of high dimensional dyadic data. In this paper, we present a method to combine these two techniques together by introducing a new matrix tri-factorization based formulation for semi-supervised latent semantic learning, which can incorporate labeled information into traditional unsupervised learning of latent semantics. Our inspiration for multi-task semantic feature learning comes from two facts, i.e., 1) multiple tasks generally share a set of common latent semantics, and 2) a semantic usually has a stable indication of categories no matter which task it is from. Thus to make multiple tasks learn from each other we wish to share the associations between categories and those common semantics among tasks. Along this line, we propose a novel joint Nonnegative matrix tri-factorization framework with the aforesaid associations shared among tasks in the form of a semantic-category relation matrix. Our new formulation for multi-task learning can simultaneously learn (1) discriminative semantic features of each task, (2) predictive structure and categories of unlabeled data in each task, (3) common semantics shared among tasks and specific semantics exclusive to each task. We give alternating iterative algorithm to optimize our objective and theoretically show its convergence. Finally extensive experiments on text data along with the comparison with various baselines and three state-of-the-art multi-task learning algorithms demonstrate the effectiveness of our method.

关键词： Semantics Optimization Feature extraction Data mining Data models Joints Iterative methods

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Mining association rules on qing court medical records: Semantic abstraction and standardization

Mining association rules on qing court medical records: Sema...

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2012 IEEE 12th International Conference on Computer and Information Technology, CIT 2012

作者： Cao, Cong Wang, Weimin Cao, Cungen Zang, Liangjun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9780769548586

To explore the association relations among disease, pathogenesis, physician, symptoms and drug, we adapt a variational Apriori algorithm for discovering association rules on a dataset of the Qing Court Medical Records. There are five types of semantic associations we intend to discover, including Disease-Pathogenesis-Drug set(DPaD), Disease-Symptoms-Drug set (DSyD), Disease-Drug set (DD), Disease-Physician-Drug set (DPhD) and Disease-Drug Category Set (DDC). To solve the synonymity problem and the data sparseness problem, we give a mapping strategy which maps pathogenesis to standardized forms and maps drugs to drug categories. With the mapping strategy the number of frequent drug sets rises from 287 to 1184. The experimental results indicate that our method with the mapping strategy is an effective way to acquire valuable semantic association rules. © 2012 IEEE.

关键词： Association rules

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A Cellular-Assisted Efficient Handover Algorithm for Wireless Sensor Networks

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International Journal of Communications, Network and System Sciences 2012年第10期5卷 708-713页

作者： Jun Zhu Hui Gao Yuling Ouyang Key Laboratory of Intelligent Computing & Singnal Processing Ministry of Education Anhui University Hefei China Shanghai Research Center for Wireless Communications Shanghai China

Heterogeneous network convergence and handover have become very hot in recent years. This paper proposed an efficient handover scheme in Multi-PAN Wireless Sensor Networks (WSNs). A number of edge nodes are set at the edge of each Personal Area Networks (PANs). A user equipment (UE), which has WSN and cellular network interface, acts as sensor node or mobile cluster head in WSN area. Thus, edge early warning can be acquired from edge nodes and neighbor channel information can be acquired with BS-assistance. Simulation results show that low transmission interrupted delay and low energy consumption can be achieved compared with conventional scheme in WSN.

关键词： Efficient Handover Edge Early Warning Special Beacon Wireless Sensor Networks

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Optic disc localization by projection with vessel distribution and appearance characteristics

Optic disc localization by projection with vessel distributi...

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International Conference on Pattern Recognition

作者： Dongbo Zhang Yao Yi Xingyu Shang Yinghui Peng Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education The College of Information Engineering Xiangtan University China

ISBN: (纸本)9781467322164

Considering the vessel distribution and optic disc (OD) appearance characteristics comprehensively, a novel OD localization method based on 1-D projection is proposed. The horizontal location is determined by vascular scatter degree, an evaluation index of vessel distribution. And the vertical location is found by brightness and edge gradient around OD. The proposed method was tested on four publicly-available databases and a self-selection database. The OD was successfully located in 357 images out of 380 images (94%). And the proposed method shows good robustness in both normal and diseased images.

关键词： Databases Retina Optical imaging Biomedical optical imaging Optical filters Optical scattering Image segmentation

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Leaf image recognition using Fourier transform based on ordered sequence

Leaf image recognition using Fourier transform based on orde...

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8th International Conference on intelligent computing Technology, ICIC 2012

作者： Yang, Li-Wei Wang, Xiao-Feng Department of Automation University of Science and Technology of China Hefei Anhui 230027 China Intelligent Computing Laboratory Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O. Box 1130 Hefei Anhui 230031 China Key Lab. of Network and Intelligent Information Processing Hefei University Hefei Anhui 230601 China

ISBN: (纸本)9783642315879

There are a number of leaf recognition methods, but most of them are based on Euclidean space. In this paper, we will introduce a new description of feature for the leaf image recognition, which represents the leaf contour with the ordered sequence. For a leaf image, points on the contour represent the most important information of the leaf. Thus, by extracting serial points of the leaf contour, the unique corresponding ordered sequence can be obtained for a contour. Then, we can compute the amplitude-frequency feature by performing the Discrete Fourier transform on the ordered sequence. Since the low-frequency part of the Fourier transform represents the global information and the high-frequency part the local details, we can adopt the amplitude-frequency feature for leaf image recognition. Experimental results on the famous Swedish library and ICL library show that the proposed feature is effective for leaf image recognition. © 2012 Springer-Verlag.

关键词： Image recognition

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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce 1

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7th IFIP International Conference on intelligent Information processing, IIP 2012

作者： Li, Ning Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalability of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement. © 2012 IFIP International Federation for Information processing.

关键词： MapReduce

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Multi-label image annotation based on neighbor pair correlation chain 1

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8th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012

作者： Jiang, Guang Liu, Xi Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China Information Technology Laboratory Fujitsu Research and Development Center Co. Ltd. Beijing 100025 China

ISBN: (数字)9783642315374

ISBN: (纸本)9783642315367

Image annotation plays an important role in content-based image understanding, various machine learning methods have been proposed to solve this problem. In this paper, label correlation is considered as an undirected bipartite graph, in which each label are correlated by some common hidden topics. As a result, given a label, random walk with restart on the graph supplies a most related label, repeating this precedure leads to a label chain, which keep each adjacent labels pair correlated as maximally as possible. We coordinate the labels chain with its respective classifier training on bottom feature, and guide a classifier chain to annotate an image. The experiment illustrates that our method outperform both the baseline and another popular method. © 2012 Springer-Verlag.

关键词： Learning systems

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